Online Flocking Control of UAVs with Mean-Field Approximation

We present a novel approach to the formation controlling of aerial robot swarms that demonstrates the flocking behavior. The proposed method stems from the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable control inputs from being produced and subsequently leads to feasible trajectories. By modeling the inter-agent relationships using a pairwise energy function, we show that interacting robot swarms constitute a Markov Random Field. Our algorithm builds on the Mean-Field Approximation and incorporates the collective behavioral rules: cohesion, separation, and velocity alignment. We follow a distributed control scheme and show that our method can control a swarm of UAVs to a formation and velocity consensus with real-time collision avoidance. We validate the proposed method with physical and high-fidelity simulation experiments.Comment: To appear in the proceedings of IEEE International Conference on Robotics and Automation (ICRA), 202

Graceful Navigation for Mobile Robots in Dynamic and Uncertain Environments.

The ability to navigate in everyday environments is a fundamental and necessary skill for any autonomous mobile agent that is intended to work with human users. The presence of pedestrians and other dynamic objects, however, makes the environment inherently dynamic and uncertain. To navigate in such environments, an agent must reason about the near future and make an optimal decision at each time step so that it can move safely toward the goal. Furthermore, for any application intended to carry passengers, it also must be able to move smoothly and comfortably, and the robot behavior needs to be customizable to match the preference of the individual users. Despite decades of progress in the field of motion planning and control, this remains a difficult challenge with existing methods. In this dissertation, we show that safe, comfortable, and customizable mobile robot navigation in dynamic and uncertain environments can be achieved via stochastic model predictive control. We view the problem of navigation in dynamic and uncertain environments as a continuous decision making process, where an agent with short-term predictive capability reasons about its situation and makes an informed decision at each time step. The problem of robot navigation in dynamic and uncertain environments is formulated as an on-line, finite-horizon policy and trajectory optimization problem under uncertainty. With our formulation, planning and control becomes fully integrated, which allows direct optimization of the performance measure. Furthermore, with our approach the problem becomes easy to solve, which allows our algorithm to run in real time on a single core of a typical laptop with off-the-shelf optimization packages. The work presented in this thesis extends the state-of-the-art in analytic control of mobile robots, sampling-based optimal path planning, and stochastic model predictive control. We believe that our work is a significant step toward safe and reliable autonomous navigation that is acceptable to human users.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120760/1/jongjinp_1.pd

MS

thesisIn this research, a computerized motion planning and control system for multiple robots is presented. Medium scale wheeled mobile robot couriers move wireless antennas within a semicontrolled environment. The systems described in this work are integrated as components within Mobile Emulab, a wireless research testbed. This testbed is publicly available to users remotely via the Internet. Experimenters use a computer interface to specify desired paths and configurations for multiple robots. The robot control and coordination system autonomously creates complex movements and behaviors from high level instructions. Multiple trajectory types may be created by Mobile Emulab. Baseline paths are comprised of line segments connecting waypoints, which require robots to stop and pivot between each segment. Filleted circular arcs between line segments allow constant motion trajectories. To avoid curvature discontinuities inherent in line-arc segmented paths, higher order continuous polynomial spirals and splines are constructed in place of the constant radius arcs. Polar form nonlinear state feedback controllers executing on a computer system connected to the robots over a wireless network accomplish posture stabilization, path following and trajectory tracking control. State feedback is provided by an overhead camera based visual localization system integrated into the testbed. Kinematic control is used to generate velocity commands sent to wheel velocity servo loop controllers built into the robots. Obstacle avoidance in Mobile Emulab is accomplished through visibility graph methods. The Virtualized Phase Portrait Method is presented as an alternative. A virtual velocity field overlay is created from workspace obstacle zone data. Global stability to a single equilibrium point, with local instability in proximity to obstacle regions is designed into this system

Visual flight rules-based collision avoidance systems for UAV flying in civil aerospace

The operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation authorities, which require full compliance with regulations that apply for manned aircraft. This paper proposes control algorithms for a collision avoidance system that can be used as an advisory system or a guidance system for UAVs that are flying in civil airspace under visual flight rules. A decision-making system for collision avoidance is developed based on the rules of the air. The proposed architecture of the decision-making system is engineered to be implementable in both manned aircraft and UAVs to perform different tasks ranging from collision detection to a safe avoidance manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are proposed based on pilot suggestions for a subset of possible collision scenarios. The proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal collision avoidance algorithm is developed for real-time local trajectory planning. Essentially, a finite-horizon optimal control problem is periodically solved in real-time hence updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal control problem is formulated in output space, and parameterized by using B-splines. Then the optimal designed outputs are mapped into control inputs of the system by using the inverse dynamics of a fixed wing aircraft

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

Path Planning Algorithms for Autonomous Mobile Robots

This thesis work proposes the development and implementation of multiple different path planning algorithms for autonomous mobile robots, with a focus on differentially driven robots. Then, it continues to propose a real-time path planner that is capable of finding the optimal, collision-free path for a nonholonomic Unmanned Ground Vehicle (UGV) in an unstructured environment. First, a hybrid A* path planner is designed and implemented to find the optimal path; connecting the current position of the UGV to the target in real-time while avoiding any obstacles in the vicinity of the UGV. The advantages of this path planner are that, using the potential field techniques and by excluding the nodes surrounding every obstacles, it significantly reduces the search space of the traditional A* approach; it is also capable of distinguishing different types of obstacles by giving them distinct priorities based on their natures and safety concerns. Such an approach is essential to guarantee a safe navigation in the environment where humans are in close contact with autonomous vehicles. Then, with consideration of the kinematic constraints of the UGV, a smooth and drivable geometric path is generated. Throughout the whole thesis, extensive practical experiments are conducted to verify the effectiveness of the proposed path planning methodologies

A Model Predictive Path Integral Method for Fast, Proactive, and Uncertainty-Aware UAV Planning in Cluttered Environments

Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel receding-horizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory -- a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.Comment: Accepted to IROS 2023, 8 page

Analysis of inverse simulation algorithms with an application to planetary rover guidance and control

Rover exploration is a contributing factor to driving the relevant research forward on guidance, navigation, and control (GNC). Yet, there is a need for incorporating the dynamic model into the controller for increased accuracy. Methods that use the model are limited by issues such as linearity, systems affine in the control, number of inputs and outputs. Inverse Simulation is a more general approach that uses a mathematical model and a numerical scheme to calculate the control inputs necessary to produce a desired response defined using the output variables. This thesis develops the Inverse Simulation algorithm for a general state space model and utilises a numerical Newton-Raphson scheme to converge to the inputs using two approaches: The Differentiation method converges based on the state and output equations. The Integration method converges based on whether the output matches the desired and is suitable for grey or black-box models. The thesis offers extensive insights into the requirements and application of Inverse Simulation and the performance parameters. Attention is given to how the inputs and outputs affect the Jacobian formulation and ensure an efficient solution. The linear case and the relationship with feedback linearisation are examined. Examples are given using simple mechanical systems and an example is also given as to how Inverse Simulation can be used for determining system input disturbances. Inverse Simulation is applied for the first time for guidance and control of a fourwheeled, differentially driven rover. The desired output is the time history of the desired trajectory and is used to produce the required control inputs. The control inputs are nominal and are applied to the rover without additional correction. Using insights from the system’s physics and actuation, the Differentiation and Integration schemes are developed based on the general method presented in this thesis. The novel Differentiation scheme employs a non-square Jacobian. The method provides very accurate position and orientation control of the rover while considering the limitations of the model used. Finally, the application of Inverse Simulation to the rover is supported by a review of current designs that resulted in a rover taxonomy

Control, estimation, and planning algorithms for aggressive flight using onboard sensing

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 107-111).This thesis is motivated by the problem of fixed-wing flight through obstacles using only on-board sensing. To that end, we propose novel algorithms in trajectory generation for fixed-wing vehicles, state estimation in unstructured 3D environments, and planning under uncertainty. Aggressive flight through obstacles using on-board sensing involves nontrivial dynamics, spatially varying measurement properties, and obstacle constraints. To make the planning problem tractable, we restrict the motion plan to a nominal trajectory stabilized with an approximately linear estimator and controller. This restriction allows us to predict distributions over future states given a candidate nominal trajectory. Using these distributions to ensure a bounded probability of collision, the algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration. This process results in a search tree in belief space that provably converges to the optimal path. We analyze the algorithm theoretically and also provide simulation results demonstrating its utility for balancing information gathering to reduce uncertainty and finding low cost paths. Our state estimation method is driven by an inertial measurement unit (IMU) and a planar laser range finder and is suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaining accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. The localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 25x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. Using a multi-step forward fitting method we are able to identify the noise parameters of the IMU leading to high quality predictions of the uncertainty associated with the process model. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment. The algorithm for generating the trajectories used in the planning process computes a transverse polynomial offset from a nominal Dubins path. The polynomial offset allows us to explicitly specify transverse derivatives in terms of linear equality constraints on the coefficients of the polynomial, and minimize transverse derivatives by using a Quadratic Program (QP) on the polynomial coefficients. This results in a computationally cheap method for generating paths with continuous heading, roll angle, and roll rate for the fixed-wing vehicle, which is fast enough to run in the inner loop of the RRBT.by Adam Parker Bry.S.M